A particular problem arises in a communications links where a transmitter with more than one transmit antenna is employed since signals received from different transmit antennas interfere with one another. This results in so-called multi-stream interference (MSI) and causes decoding difficulties. The potential advantage, however, is greatly increased throughput (that is, a higher bit rate) for such a communications link. In this type of MIMO (Multiple-input Multiple-output) communication link the “input” (to a matrix channel) is provided by the transmitter's plurality of transmit antennas and the “output” (from a matrix channel) is provided by a plurality of receive antennas. Thus each receive antenna receives a combination of signals from all the transmitter's transmit antennas which must be unscrambled.
A typical wireless network comprises a plurality of mobile terminals (MT) each in radio communication with an access point (AP) or base station of the network. The access points are also in communication with a central controller (CC) which in turn may have a link to other networks, for example a fixed Ethernet-type network. Until recently considerable effort was put into designing systems so as to mitigate for the perceived detrimental effects of multipath propagation, especially prevalent in wireless LAN (local area network) and other mobile communications environments. However the described work G. J. Foschini and M. J. Gans, “On limits of wireless communications in a fading environment when using multiple antennas” Wireless Personal Communications vol. 6, no. 3, pp. 311-335, 1998 has shown that by utilising multiple antenna architectures at both the transmitter and receiver (a so-called multiple-input multiple-output (MIMO) architecture) vastly increased channel capacities are possible. Attention has also turned to the adoption of space-time coding techniques (in OFDM, space-frequency coding) for wideband channels. Typically channel state information (CSI) for maximum likelihood detection of such coding is acquired via training sequences and the resulting CSI estimates are then fed to a Viterbi decoder.
A technique for space-time code detection in a MIMO system based upon the use of periodic pilot sequences and interpolation filters is described in A. Naguib, V. Tarokh, N Seshadri and A. Calderbank “A space-time coding based model for high data rate wireless communications” IEEE J-SAC vol. 16, pp. 1459-1478. October 1998.
FIG. 1 shows a MIMO communication system 100 in the context of which the technique described in Naguib et al operates. An information source 101 provides an information symbol s(1) at time 1 to a space-time encoder 102 which encodes the symbol as N code symbols c1(1) c2(1) . . . , cN(1)), each of which is transmitted simultaneously from one of transmit antennas 104. A plurality M of receive antennas 106 receives respectively signals r1(1), . . . rM(1) which are input to receiver 108. The receiver 108 provides on output 110 an estimate ŝ(1) of the encoded transmitted symbol ŝ(1). There is a plurality of channels between the transmit and receive antennas, for example all channels with two transmit antennas and two receive antennas. The technique described in Naguib et al uses periodic pilot sequences in the transmitted signal to estimate the time varying responses of these channels.
Third generation mobile phone networks use CDMA (Code Division Multiple Access) spread spectrum signals for communicating across the radio interface between a mobile station and a base station. These 3G networks are encompassed by the International Mobile Telecommunications IMT-2000 standard (www.ituint). Collectively the radio access portion of a 3G network is referred to as UTRAN (Universal Terrestrial Radio Access Network) and a network comprising UTRAN access networks is known as a UMTS (Universal Mobile Telecommunications System) network. The UMTS system is the subject of standards produced by the Third Generation Partnership Project (3GPP, 3GPP2), technical specifications for which can be found at www.3gpp.org. Fourth generation mobile phone networks, although not yet defined, may employ MIMO-based techniques.
In practical data communication systems multipath within a channel results in intersymbol interference (ISI), which is often corrected with a combination of equalisation and forward error coding. For example a linear equaliser effectively convolves the received data with an inverse of the channel impulse response to produce data estimates with ISI substantially removed. An optimal equaliser may employ maximum likelihood (ML) sequence estimation or maximum a priori estimation (MAP), for example using a Viterbi algorithm. Where data has been protected with a convolutional code a soft input Viterbi decoder may be employed, usually together with data interleaving to reduce the effects of burst errors. Such approaches provide optimal equalisation but become impractical as the symbol alphabet size and sequence length (or equivalently channel impulse response length) increases.
Turbo equalisation achieves results which are close to optimal but with substantially reduced complexity compared to non-iterative joint channel equalisation and decoding. Broadly speaking turbo equalisation refers to an iterative process in which soft (likelihood) information is exchanged between an equaliser and a decoder until a consensus is reached. The effect of the channel response on the data symbols is treated similarly to an error correction code and typically a soft output Viterbi algorithm (SOVA) is used for both. Again, however, such techniques are impractically complex for large delay spreads and symbol alphabets, particularly as several processing iterations may be needed to achieve convergence for a single data block. These difficulties are significantly exacerbated where signals from more than one transmit antenna must be disentangled and equalised, with a different channel response for each transmit antenna or transmit-receive antenna pair.
A paper by Tuchler et al. (Minimum Mean Squared Error Equalization Using A-priori Information, Michael Tuchler, Andrew Singer, Ralf Koetter, IEEE Transactions on Signal Processing, vol. 50, pp. 673-683, March 2002) describes a simplified approach to turbo-equalisation where a single transmit antenna is employed. In this paper the conventional MAP equaliser is replaced by a linear equaliser (that is by a linear or transversal filter) with filter coefficients which are updated using a minimum mean square error (MMSE) criterion evaluated over both the distribution of noise and the distribution of symbols. A linear estimate {circumflex over (x)}n of a transmitted symbol xn is determined using an observation zn via the equation {circumflex over (x)}n=anHzn+bn where superscript H denotes the Hermitian operator and an and bn are the coefficients of the estimator (strictly the estimate should be termed affine rather than linear because of the constant bn). The coefficients are chosen to minimise the MSE cost E(|xn−{circumflex over (x)}n|2) where E(•) denotes a mean or expectation value. As information is fed back to the equaliser from the error correction decoder the filter coefficients change with time and are thus recomputed for each data symbol to be estimated. A related technique is described in WO 02/15459.
The contents of the Tuchler et al. paper are helpful for understanding the present invention, which builds upon and extends this work and, in particular, sections II and III of this paper are specifically hereby incorporated by reference.
Another paper, Tetsushi Abe and Tad Matsumoto, “Space-Time Turbo Equalization and Symbol Detection in Frequency Selective MIMO Channels” in: Proc. Veh. Techn. Conference, IEEE VTS 5th. Vol. 2. pg 1230-1234, 2001 describes the application of turbo equalisation to a MIMO system with a plurality of users (transmitters). However the simplified approach described in this paper is only suitable for BPSK and not for other (higher) modulation schemes used, for example, by wireless LAN networks. Therefore improved techniques, and in particular improved algorithms for systems with multiple antenna transmitters are still desirable.
Other background prior art may be found in US 2002/0110188 which also employs adaptive linear filtering in SISO (soft-in soft-out) turbo equalisation, in Iterative (Turbo) Soft Interference Cancellation and Decoding for Coded CDMA, Xiaodong Wang, H. Vincent Poor, IEEE Trans. Comms. Vol 47, No. 7, July 1999, pp. 1046-1061, and in Iterative Receivers for Multiuser Space-Time Coding Systems, Ben Lu and Xiaodong Wang, IEEE Journal On Selected Areas in Communications, Vol. 18 No. 11, November 2000. pp. 2322-2335. In these documents the references to “soft” information are to information relating to the likelihood of a particular bit, symbol or signal value as opposed to, for example, hard information resulting from a hard bit decision defining a bit as a logic one or zero. More complex trellis-based and Bayesian-based SISO equaliser components have also been utilized for iterative turbo-equalization where the equalizer and decoder iteratively exchange transmitted symbol likelihood information to improve the bit-error-rate of the receiver.
Filter-based equalisers using MMSE or zero-forcing criteria, Bayesian-based equalisers and trellis-based equalisers such as (MAP) and Maximum Likelihood Sequence Estimation (MLSE) equalisers have all been employed for wideband MIMO systems to mitigate both ISI and MSI. It is also known to use iterative turbo equalization technology for equalisation in MIMO systems. However turbo equalisers employing trellis or Bayesian-based SISO equalisation have a high computational complexity, and this increases exponentially with the length of channel response and the number of possible states/levels of the modulation employed. The simplified turbo equalisation schemes described in Tuchler et al., WO 02/15459 and US 2002/0110188 consider systems with a plurality of receive antennas but are incapable of equalisation in systems with more than one transmit antenna. It will be appreciated that with multiple receive antennas and a single transmit antenna each receive antenna is associated with a single channel whereas the situation is much more complex with multiple transmit antennas since a combination of signals from different transmit antennas is received.
There is therefore a need for a reduced computational complexity turbo equalization scheme suitable for mitigating both MSI and ISI in a MIMO communications system.